Millions of people communicate every day through the Internet and wireless phone networks using text messages, micro-blogs, blogs, posts to social networks, and posts to a variety of other networks of connected devices. In some instances, individuals specify the “topic” of their message using a hashtag, which consists of a word or phrase in conjunction with a hash symbol (#). For example, a baseball fan in Washington, D.C. might tweet a message on Twitter such as “Werth killed it today at the #Nats game.” A driver in New York might post a status update on Facebook such as “Was stuck in traffic for 2 hours this morning! #gridlock”. This makes it easier for other users, computers, and companies to identify the specific topics of the messages, track what topics people are messaging about, and provide relevant content back to users about specific topics they are, or may be, interested in.
Many companies, advertisers, service providers, and other users seek to similarly understand the sentiment (i.e., the opinion, positivity or negativity, or other emotion) being expressed in these messages. Sentiment tracking is a major component of market research polls and surveys. Sentiment analysis allows companies, politicians, celebrities, scientists, and researchers to better understand what individuals or populations think about a given brand, topic, or person. The sentiment of a user, however, is not necessarily easy to identify within a message. While humans can generally determine the sentiment being expressed, computers must use complex algorithms and language processing software in an attempt to deduce a user's sentiment. These systems are often flawed due to the assumptions they have to make and to the complexities of humor, sarcasm, and slang. In light of this, there is a need for methods and systems to allow users to easily identify their precise sentiment and to make these sentiments recordable and trackable by both humans and computers.